Operational and safety goals for the built environment demand robust, scalable and reliable large scale monitoring for infrastructure systems. High performance real-time event detection and decision making requires models and algorithms to process large amounts of data from dense sensor networks deployed in these systems. Despite advances in the development of detection algorithms for such networks, there are two widely recognized and conflicting obstacles: detection rules need to be sufficiently complex to adapt to the spatiotemporal changes in the environment, requiring the sharing of data; but rules are constrained by statistical performance guarantees and computation and communicational budgets imposed by the network. This project addresses these challenges by developing a fundamentally new approach that jointly accounts for statistical detection, communication constraints and distributed computation. This research develops a framework that integrates the distributed computation and communication constraints of the underlying network infrastructure with flexible stochastic modeling and learning algorithms with spatiotemporal data. The modeling and algorithms enable simultaneous and sequential decision making at many local sites, by borrowing information across the network in a statistically coherent and computationally efficient manner. Combining the formalism of sequential change point detection, nonparametric and probabilistic graphical models and spatiotemporal statistics, the project develops distributed and sequential message-passing algorithms for detecting changes in the underlying distributions generating network data. The models developed also offer new theoretical understanding of the trade-offs between statistical model complexity, distributed computation efficiency, and structure of communication constraints within the network.

This interdisciplinary research brings together students and researchers from different areas, utilizing and developing knowledge and cross-disciplinary skills in the fields of computer science, statistics, signal processing and civil engineering.

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Stanford University
Palo Alto
United States
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